gnu: Add python-fast-histogram.
* gnu/packages/python-science.scm (python-fast-histogram): New variable. Change-Id: Ibecdd87db9c132052a8eca76c01108c4f6e624c4
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@ -1545,6 +1545,38 @@ region of practical equivalence (rope), or that the second classifier has
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higher scores.")
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(license license:expat)))
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(define-public python-fast-histogram
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(package
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(name "python-fast-histogram")
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(version "0.14")
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(source
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(origin
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(method url-fetch)
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(uri (pypi-uri "fast_histogram" version))
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(sha256
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(base32 "1sk9xa85cgm4sylzblwv3qr2dmm0ic06zkwxqa2xlazjiawp629r"))))
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(build-system pyproject-build-system)
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(arguments
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(list
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#:phases
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#~(modify-phases %standard-phases
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(add-before 'check 'build-extensions
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(lambda _
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(invoke "python" "setup.py" "build_ext" "--inplace"))))))
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(propagated-inputs (list python-numpy))
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(native-inputs (list python-hypothesis python-pytest))
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(home-page "https://github.com/astrofrog/fast-histogram")
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(synopsis "Fast simple 1D and 2D histograms")
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(description
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"The fast-histogram mini-package aims to provide simple and fast
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histogram functions for regular bins that don't compromise on performance. It
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doesn't do anything complicated - it just implements a simple histogram
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algorithm in C and keeps it simple. The aim is to have functions that are fast
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but also robust and reliable. The result is a 1D histogram function here that
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is 7-15x faster than @code{numpy.histogram}, and a 2D histogram function that
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is 20-25x faster than @code{numpy.histogram2d}.")
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(license license:bsd-3)))
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(define-public python-fastcluster
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(package
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(name "python-fastcluster")
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